基于稀疏成份分析的逆合成孔径雷达成像技术

ISAR Imaging Techniques Based on Sparse Component Analysis

根据小角度条件下的逆合成孔径雷达观测模型,利用稀疏成份分析方法给出了基于FFT的二维联合超分辨算法和二维解耦超分辨成像算法.该算法能从补偿后的较低分辨率测量数据中获得更高分辨率的ISAR图像,提高图像的清晰度,凸现目标的特征结构,有利于目标识别.同时,二维解耦算法能与运动补偿过程相结合,以提高补偿精度.针对典型空间目标的成像结果表明,基于FFT的二维联合算法获得的图像较为干净,目标背景对比度高;二维解耦算法运算速度更快.算法能满足实时或准实时成像的要求.

Based on the measurement model of inverse synthetic aperture radar within a small aspect sector,two imaging methods, named as FFr-based united algorithm and two-dimensional decoupled algorithm, are presented with the application of sparse component analysis. These methods can form ISAR images with higher resolution from com- pensated incomplete measured data,improve the clarity of the images and make the feature structure much clear which are helpful for target recognition. The decoupled algorithm can be combined with the process of motion compensation so as to improve the compensation precision. The numerical results of a typical space target indicate that FFF-based united algorithm can provide clear ISAR images with high contrast and the decoupled algorithm is computationally efficient. Both algorithms can meet the demand of real-time or quasi-real-time imaging.